River Ice Fine-Grained Segmentation: A GF-2 Satellite Image Dataset and Deep Learning Benchmark
Wei C. Li H. Chen L. Zhou H. Taukebayev O. Wu W. Temirbayev A. Han L. Ran L. Yin H. Wang P. Liu J. Zhang X. Zhang Y.
2025Institute of Electrical and Electronics Engineers Inc.
IEEE Transactions on Geoscience and Remote Sensing
2025#63
Semantic segmentation of river ice images serves as a critical technological foundation for hydrological monitoring and an ice flood early warning system. Current publicly available river ice datasets predominantly utilize UAV-captured images and ground-based photographic observations. To address the limitations of spatial coverage in existing datasets, we present NWPU_YRCC_GFICE—a satellite remote sensing dataset constructed from multispectral GF-2 satellite images. The dataset innovatively categorizes river ice into six fine-grained classes across freeze–thaw cycles and covers river ice data from the Yellow River (Ningxia-Inner Mongolia section) spanning the past ten years. We further establish a comprehensive deep learning benchmark, which evaluates 33 state-of-the-art segmentation models and two improved segmentation models based on YOLO and SegFormer architectures, separately. Experiments are conducted on the NWPU_YRCC_GFICE dataset and three public river ice datasets (NWPU_YRCC_EX, NWPU_YRCC2, and Alberta river ice segmentation datasets). The proposed models exhibit excellent performance, surpassing the state-of-the-art methods. The presented NWPU_YRCC_GFICE dataset and the benchmark enrich the river ice dataset and favor promoting fine-grained river ice segmentation research from satellite view.
Fine-grained semantic segmentation , river ice dataset , SegFormer , YOLO
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Ningbo Institute of Northwestern Polytechnical University, Northwestern Polytechnical University, School of Computer Science, Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, National Engineering Laboratory for Integrated Aerospace-Ground Ocean Big Data Application Technology, Xi’an, 710072, China
Information Center of Yellow River Conservancy Commission, Zhengzhou, 450004, China
Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Zhengzhou Normal University, School of Geography and Tourism, Zhengzhou, 450044, China
Ningbo Institute of Northwestern Polytechnical University
Information Center of Yellow River Conservancy Commission
Al-Farabi Kazakh National University
Zhengzhou Normal University
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